Orient
0–10
Read the task and the code, run the existing tests, and state your plan before asking the assistant anything.
Evidence: A short map of the files and the change you intend to make.
Who this is for
Software engineers whose Meta onsite includes the AI-enabled coding round, and anyone preparing for AI-assisted coding interviews at other companies.
Use the mock interview deliberately
What this practice should reveal
In July 2025 Meta confirmed that it was testing coding interviews in which candidates can use an AI assistant; an internal post reported at the time said this is closer to the environment its engineers work in (TechRadar, citing 404 Media). Since then, candidate reports published by interview-prep sites describe a round of about an hour with an AI assistant built into the editor, and Hello Interview’s write-up describes a task that builds on an existing multi-file codebase rather than a single LeetCode-style function.
Treat every detail beyond Meta’s confirmation as reported, not official, and confirm the format, languages and tools with your recruiter. What the reports agree on is the skill being tested: you still have to understand the problem, decide what to hand to the assistant, read and test what it writes, and explain your decisions. Accepting generated code you have not checked is the failure to practice against.
Core capabilities
What to practice under pressure
1
Reading unfamiliar code fast
Find the entry point, the data models and the tests before changing anything, and say out loud what each file is for.
2
Delegating in small, checkable steps
Give the assistant one bounded task at a time, such as a function, a test or a bug, with the context it needs.
3
Verifying what the assistant writes
Read every change, run the tests, and look for the usual mistakes: off-by-one errors, the wrong complexity, invented APIs and missed edge cases.
4
Owning the solution
Be ready to explain why the code is correct, what you checked and what you would do next, without leaning on the assistant.
Timed session
A 60-minute mock interview plan
PhaseTimeActivityEvidence to produce
Fix
10–20
Find and fix the first failing behavior, using the assistant only for well-scoped questions.
Evidence: A passing test you can explain line by line.
Build
20–45
Implement the main feature in small steps, reading and running each change the assistant suggests.
Evidence: Working code with tests for the edge cases you named.
Improve
45–60
Handle larger inputs or a new requirement, and explain the complexity of the final version.
Evidence: A measured improvement and a clear trade-off.
Live practice
Run these interview scenarios
The scenarios are independent practice. Open the public prompt first, then run it in the matching workspace with voice follow-ups and evidence-based review.
Hard · ai assisted coding
Gated Maze Pathfinder
Fix defects in an existing maze-routing codebase, build a shortest-path search that respects one-way gates, then make it fast enough for mazes of up to a million cells.
Hard · ai assisted coding
Unique-Letter Word Picker
Fix defects in a small word-game codebase, write an exact search that picks words sharing no letter for the largest total length, then make it fast on large word lists.
Medium · ai assisted coding
Concurrent Eviction Cache
Implement a bounded cache with recency-based eviction, then reason through thread safety, race conditions, and production hardening.
Medium · ai assisted coding
Delivery Payment Ledger
Build a driver payment ledger in three phases: exact delivery costs from hourly rates and start and end times, payouts that never pay a delivery twice, and a 100,000-delivery performance tier.
Hard · ai assisted coding
Warehouse Robot Grid Navigator
Build an extensible warehouse navigation service, then evolve it from one robot to multiple robots with collision handling.
Hard · ai assisted coding
Airfield Operations Controller
Design and implement a small airfield scheduler that sequences takeoffs and landings while handling runway conflicts and changing priorities with help from an AI assistant.
Self-review scorecard
Look for evidence, not confidence
Problem solving
Strong signal: Breaks the task into steps and decides what to delegate.
Warning signal: Pastes the whole task into the assistant and hopes.
Code quality
Strong signal: Keeps to the existing structure and writes code a teammate could maintain.
Warning signal: Accepts large generated changes that ignore the codebase.
Verification
Strong signal: Runs tests after each change and catches the assistant’s mistakes.
Warning signal: Trusts output that has not been read or run.
Communication
Strong signal: Narrates intent, checks and trade-offs as they happen.
Warning signal: Works silently, then cannot explain the final code.
Frequently asked questions
Meta AI-Assisted Coding Interview FAQ
Does Meta let candidates use AI in coding interviews?
Meta confirmed in July 2025 that it was testing coding interviews in which candidates can use an AI assistant. Candidate reports since then describe one AI-enabled coding round in the onsite; ask your recruiter whether your loop includes it and which tools you will have.
How is it different from a LeetCode-style round?
Reports describe working inside an existing codebase with an assistant in the editor, in stages such as fixing a bug, building a feature and improving performance, instead of solving one function from scratch. You are still expected to understand and verify all of the code.
How can I practice an AI-assisted coding interview?
Practice with a live interviewer and an assistant in the same environment, so that someone watches how you delegate and verify. EngMock’s AI-assisted coding mock interviews put you in a repository with its own coding assistant while a voice interviewer asks follow-ups; two of them are based on candidate reports from Meta.
Are these Meta’s real interview questions?
No. They are independently written practice scenarios based on public candidate reports, and they are not official or leaked questions. EngMock is not affiliated with Meta.